Livestock Dynamic Responses to Climate Change in Alpine Grasslands on the Northern Tibetan Plateau: Forage Consumption and Time-Lag Effects

Feng, Ying; Zhang, Xianzhou; Shi, Peili; Fu, Gang; Zhang, Yangjian; Zhao, Guangshuai; Zeng, Chaoxu; Zhang, Jing

doi:10.5814/j.issn.1674-764x.2017.01.011

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…So, our review focuses on the impact of climatic factors on cross-scale ecological organizations, assuming that climate influences on vegetation dynamics in the Tibetan Plateau are more vigorous than anthropogenic influences. However, the impact of anthropogenic activities on plateau health cannot be neglected [ 158 ] and might need a separate synthesis of the published knowledge.…”

Section: Concluding Remarks and Further Directionmentioning

confidence: 99%

Climate-Driven Plant Response and Resilience on the Tibetan Plateau in Space and Time: A Review

Bhattarai

Zheng

Bhatta

et al. 2021

Plants

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Climate change variation on a small scale may alter the underlying processes determining a pattern operating at large scale and vice versa. Plant response to climate change on individual plant levels on a fine scale tends to change population structure, community composition and ecosystem processes and functioning. Therefore, we reviewed the literature on plant response and resilience to climate change in space and time at different scales on the Tibetan Plateau. We report that spatiotemporal variation in temperature and precipitation dynamics drives the vegetation and ecosystem function on the Tibetan Plateau (TP), following the water–energy dynamics hypothesis. Increasing temperature with respect to time increased the net primary productivity (NPP) on most parts of the Tibetan Plateau, but the productivity dynamics on some parts were constrained by 0.3 °C decade−1 rising temperature. Moreover, we report that accelerating studies on plant community assemblage and their contribution to ecosystem functioning may help to identify the community response and resilience to climate extremes. Furthermore, records on species losses help to build the sustainable management plan for the entire Tibetan Plateau. We recommend that incorporating long-term temporal data with multiple factor analyses will be helpful to formulate the appropriate measures for a healthy ecosystem on the Tibetan Plateau.

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Section: Concluding Remarks and Further Directionmentioning

confidence: 99%

Climate-Driven Plant Response and Resilience on the Tibetan Plateau in Space and Time: A Review

Bhattarai

Zheng

Bhatta

et al. 2021

Plants

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“…Moreover, as, topography influences the distribution and productivity of AGB productivity ( Jin et al 2019 ), it also influences sustainable grazing intensity ( Sanaei et al 2019 ). Analyses based on remote sensing images without considering biomass availability of different vegetation types ( Qian et al 2007 ;Feng et al 2017 ;Zhang et al 2018b ) may overestimate livestock carrying capacity (LCC), defined as the maximum livestock number that an area of grassland can support and develop sustainably ( McKeon et al 2009 ;Long et al 2010 ;Allen et al 2011 ). Microtopography, which determines soil moisture and temperature, significantly affects the local-scale AGB distribution in grassland ecosystems ( Han et al 2016 ;Wei et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Alpine Meadow Aboveground Biomass and Sustainable Grazing in Light of Climate Warming

Yan

Gao

et al. 2023

Rangeland Ecology & Management

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“…The information of the time-lag effects of climate change on NDVI is necessary to discover the mechanisms underlying climate vegetation relationships. Furthermore, it is necessary to take the time lag into consideration in grassland management strategies [107].…”

Section: Introductionmentioning

confidence: 99%

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multi-Sensor NDVI Time-Series

et al. 2022

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This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in the years 1984–2018. NDVI is derived from MODIS and LANDSAT data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann–Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and LANDSAT data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000–2018 period in grassland at a very local scale; however, in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

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Livestock Dynamic Responses to Climate Change in Alpine Grasslands on the Northern Tibetan Plateau: Forage Consumption and Time-Lag Effects

Cited by 7 publications

References 49 publications

Climate-Driven Plant Response and Resilience on the Tibetan Plateau in Space and Time: A Review

Climate-Driven Plant Response and Resilience on the Tibetan Plateau in Space and Time: A Review

Spatiotemporal Variability of Alpine Meadow Aboveground Biomass and Sustainable Grazing in Light of Climate Warming

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multi-Sensor NDVI Time-Series

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